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Nihr

by io.github.pipeworx-io

Compare Entities

compare_entities
Read-onlyIdempotent

"Compare X and Y" / "X vs Y" / "X versus Y" / "which is bigger / better / larger / more profitable" / "rank these companies" / "head to head" — side-by-side comparison of 2–5 companies or drugs in ONE parallel call. ALWAYS PREFER over sequential single-pack lookups when comparing entities. type="company" pulls LATEST 10-K revenue + net income + cash + long-term debt from SEC EDGAR/XBRL (off-calendar fiscal years handled correctly — AAPL Sep, NVDA Jan, etc.). type="drug" pulls FAERS adverse-event counts, FDA approval counts, active trial counts. Results sorted by primary metric so "largest" / "most" / "biggest" reads off the top of the response. Returns paired data + pipeworx:// citation URIs per entity. Replaces 8–15 sequential lookups.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
typeYesEntity type: "company" or "drug".
valuesYesFor company: 2–5 tickers/CIKs (e.g., ["AAPL","MSFT"]). For drug: 2–5 names (e.g., ["ozempic","mounjaro"]).

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observed

TDQS

A4.9/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds valuable behavioral details: it pulls latest 10-K revenue/net income/cash/debt for companies (with fiscal year handling), FAERS adverse-event counts and FDA approval counts for drugs, and sorts results by primary metric. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single paragraph but packs significant information with minimal redundancy. It uses trigger phrases and dashes to structure ideas. Could be slightly more structured (e.g., bullet points) for easier parsing, but overall it is concise and front-loads the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the simple schema (2 parameters, no output schema) and rich annotations, the description covers all necessary context: what data is returned per type, how results are sorted, and the efficiency gain over sequential lookups. It is complete for an AI agent to understand when and how to use the tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, but the description enriches both parameters: it explains the enum values for 'type' (what each pulls), clarifies that 'values' should be tickers/CIKs for company and drug names for drug, and reinforces the min/max constraints. This goes beyond the schema's descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description uses specific verbs ('compare', 'side-by-side comparison') and clearly identifies the resource (2–5 companies or drugs) and the action (comparison of financial or trial data). It distinguishes itself from sibling tools like entity_profile by explicitly stating it replaces sequential single-entity lookups and by listing trigger phrases.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly states when to use this tool ('ALWAYS PREFER over sequential single-pack lookups when comparing entities') and provides clear context for each type (company vs drug), including the data sources and metrics pulled. It also specifies the range of entities (2–5) and gives example inputs.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A3.7/5.0
Disambiguation2/5

The set contains several clusters of near-overlapping tools: ask_pipeworx and ask_pipeworx_beta are explicitly identical right now, ask_pipeworx/deep_research/validate_claim all handle natural-language queries, and bet_research/polymarket_edges/polymarket_arbitrage scan the same prediction-market space. The descriptions are detailed, but that does not remove the boundary confusion.

Naming Consistency4/5

Almost all tools use lowercase snake_case with recognizable patterns such as verb_noun or prefix_domain (nihr_, polymarket_, pipeworx_). There are minor deviations like ask_pipeworx_beta vs ask_pipeworx_grounded and mixed noun/verb phrasing, but the naming is predictable overall.

Tool Count2/5

36 tools is beyond the typical well-scoped server size, and the set reads as several products bundled together: NIHR grants, Pipeworx data research, prediction markets, memory, subscriptions, and standalone utilities like generate_llms_txt or scan_dependency. Even for a broad data platform this is too many to navigate coherently, and it is a severe mismatch for a server named 'Nihr'.

Completeness3/5

Within its subdomains the set covers core workflows: query (ask/deep_research/validate), entity resolution/profile/comparison, NIHR grant lookup by several dimensions, prediction-market analysis through fill-risk, and memory/subscription lifecycles. But it is a collection of partial products rather than one coherent domain, and some outputs such as pipeworx:// citations or detected arbitrage opportunities lack an obvious in-set tool to consume them further.

Resources